class_docstr: The ClassicalTextExplainer for returning explanations for n-gram
  bag-of-words models using sklearn's classifier API.
  Also serves as extensible wrapper with components built to support addition
  of fresh explainers using certain parts of this class.
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docstr: Wrapper function for 'fit()'. If the user wants to entirely swap out
      'fit()' with a customer trainer, they can modify train instead.
  :return: A model explanation object. It is guaranteed to be a LocalExplanation.
  :return: List of length 2 . The elements are:
      * An sklearn pipeline object containing trained encoder and trained model.
      * Dict containing mapping from features to the best hyperparameters.
  :rtype: list
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code: def train(self, *args, **kwargs):
      """Wrapper function for 'fit()'. If the user wants to entirely swap out
          'fit()' with a customer trainer, they can modify train instead.
      :return: A model explanation object. It is guaranteed to be a LocalExplanation.
      :return: List of length 2 . The elements are:
          * An sklearn pipeline object containing trained encoder and trained model.
          * Dict containing mapping from features to the best hyperparameters.
      :rtype: list"""
      return self.fit(*args, **kwargs)
  
file_path: interpret-text/python/interpret_text/experimental/classical.py
imports: import numpy
  from sklearn.model_selection import GridSearchCV
  from sklearn.linear_model import LogisticRegression
  from sklearn.pipeline import Pipeline
  from interpret_text.experimental.common.base_explainer import _validate_X
  from interpret_text.experimental.common.utils_classical import BOWEncoder
  from interpret_text.experimental.common.constants import ExplainerParams
  from interpret_text.experimental.explanation import _create_local_explanation
label_desc: Use image node_id 3 for calling the ClassicalTextExplainer obj's underlying member method code with example usage: obj.train() and returns: self